Information Retrieval Studies Research Articles

Information retrieval approaches

The area of information retrieval (IR) has taken on increasing importance in recent years. This field is now of interest to large communities in several application domains (security, medicine, aeronautics, etc.). IR studies find relevant information from the semi-unstructured type of data. As the information resources generated after the search can be extensive in quantity and different in quality, it is essential to rank these results according to the degree of relevance. This paper focuses on text information retrieval (TIR) and emphasizes the importance of each IR approach. This study presents insightful aspects of TIR and provides a comparative study between some proposed approaches and models. Each model offers IR advantages and suffers from several limitations.

Information Retrieval and Knowledge Organization: A Perspective from the Philosophy of Science

Information retrieval (IR) is about making systems for finding documents or information. Knowledge organization (KO) is the field concerned with indexing, classification, and representing documents for IR, browsing, and related processes, whether performed by humans or computers. The field of IR is today dominated by search engines like Google. An important difference between KO and IR as research fields is that KO attempts to reflect knowledge as depicted by contemporary scholarship, in contrast to IR, which is based on, for example, “match” techniques, popularity measures or personalization principles. The classification of documents in KO mostly aims at reflecting the classification of knowledge in the sciences. Books about birds, for example, mostly reflect (or aim at reflecting) how birds are classified in ornithology. KO therefore requires access to the adequate subject knowledge; however, this is often characterized by disagreements. At the deepest layer, such disagreements are based on philosophical issues best characterized as “paradigms”. No IR technology and no system of knowledge organization can ever be neutral in relation to paradigmatic conflicts, and therefore such philosophical problems represent the basis for the study of IR and KO.

Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors

Increasingly, users are interacting with information retrieval (IR) systems with the goal of addressing complex, ill-defined search tasks. As a result, the interest in deconstructing and predicting search tasks has grown among IR researchers. Given the multidimensionality of search tasks, researchers usually focus on one or multiple dimensions and study the associations between implicit task dimensions and observable predictors. Synthesizing the knowledge of tasks and predictors learned from individual user studies can clarify the progresses we have made as a research community and provide an intellectual benchmark for further explorations of task-based search interactions. This article presents an overview of 76 task-based interactive IR (IIR) studies published between 2000 and 2020 and systematically coded the papers using features such as task dimensions (definitions and operationalizations), task-predictor associations, as well as task prediction models. Results include 1) data illustrating the growth and interdisciplinarity of IIR studies; 2) a comprehensive typology of task dimensions along with the associated measures; 3) a summary of the statistically significant correlations between task dimensions and predictors; 4) a list of the task dimensions being predicted, ground truth labels, and the feature sets employed. In addition, our bibliography of IIR works can be used by students and junior researchers who are new to the area. The results of our review can facilitate the growth of knowledge in IIR community and serve as the basis for future research on new modalities of user-task interactions.

Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach

With chemical libraries increasingly containing millions of compounds or more, there is a fast-growing need for computational methods that can rank or prioritize compounds for screening. Machine learning methods have shown considerable promise for this task; indeed, classification methods such as support vector machines (SVMs), together with their variants, have been used in virtual screening to distinguish active compounds from inactive ones, while regression methods such as partial least-squares (PLS) and support vector regression (SVR) have been used in quantitative structure-activity relationship (QSAR) analysis for predicting biological activities of compounds. Recently, a new class of machine learning methods - namely, ranking methods, which are designed to directly optimize ranking performance - have been developed for ranking tasks such as web search that arise in information retrieval (IR) and other applications. Here we report the application of these new ranking methods in machine learning to the task of ranking chemical structures. Our experiments show that the new ranking methods give better ranking performance than both classification based methods in virtual screening and regression methods in QSAR analysis. We also make some interesting connections between ranking performance measures used in cheminformatics and those used in IR studies.

Information Science in the web era: A term‐based approach to domain mapping

AbstractWe propose a methodology for mapping the research in Information Science (IS) field based on a combined use of symbolic (linguistic) and numeric information. Using the same list of 12 IS journals as in earlier studies on this same topic (White & McCain 1998; Zhao & Strotmann 2008a&b), we mapped the structure of research in IS for two consecutive periods: 1996‐2005 and 2006‐2008. We focused on mapping the content of scientific publications from the title and abstract fields of underlying publications. The labels of clusters were automatically derived from titles and abstracts of scientific publications based on linguistic criteria. The results showed that while Information Retrieval (IR) and Citation studies continued to be the two structuring poles of research in IS, other prominent poles have emerged: webometrics in the first period (1996‐2005) evolved into general web studies in the second period, integrating more aspects of IR research. Hence web studies and IR are more interwoven. There is still persistence of user studies in IS but now dispersed among the web studies and the IR poles. The presence of some recent trends in IR research such as automatic summarization and the use of language models were also highlighted by our method. Theoretic research on “information science” continue to occupy a smaller but persistence place. Citation studies on the other hand remains a monolithic block, isolated from the two other poles (IR and web studies) save for a tenuous link through user studies. Citation studies have also recently evolved internally to accommodate newcomers like “h‐index, Google scholar and the open access model”. All these results were automatically generated by our method without resorting to manual labeling of specialties nor reading the publication titles. Our results show that mapping domain knowledge structures at the term level offers a more detailed and intuitive picture of the field as well as capturing emerging trends.

Multitasking information seeking and searching processes

AbstractRecent studies show that humans engage in multitasking behaviors as they seek and search information retrieval (IR) systems for information on more than one topic at the same time. For example, a Web search session by a single user may consist of searching on single topics or multitasking. Findings are presented from four separate studies of the prevalence of multitasking information seeking and searching by Web, IR system, and library users. Incidence of multitasking identified in the four different studies included: (1) users of the Excite Web search engine who completed a survey form, (2) Excite Web search engine users filtered from an Excite transaction log from 20 December 1999, (3) mediated on‐line databases searches, and (4) academic library users. Findings include: (1) multitasking information seeking and searching is a common human behavior, (2) users may conduct information seeking and searching on related or unrelated topics, (3) Web or IR multitasking search sessions are longer than single topic sessions, (4) mean number of topics per Web search ranged of 1 to more than 10 topics with a mean of 2.11 topic changes per search session, and (4) many Web search topic changes were from hobbies to shopping and vice versa. A more complex model of human seeking and searching levels that incorporates multitasking information behaviors is presented, and a theoretical framework for human information coordinating behavior (HICB) is proposed. Multitasking information seeking and searching is developing as major research area that draws together IR and information seeking studies toward a focus on IR within the context of human information behavior. Implications for models of information seeking and searching, IR/Web systems design, and further research are discussed.

Task complexity, problem structure and information actions: Integrating studies on information seeking and retrieval

Analyzing actions to be supported by information and information retrieval (IR) systems is vital for understanding the needs of different types of information, search strategies and relevance assessments, in short, understanding IR. A necessary condition for this understanding is to link results from information seeking studies to the body of knowledge by IR studies. The actions to be focused on in this paper are tasks from the angle of problem solving. I will analyze certain features of work tasks and relate these features to types of information people are looking for and using in their tasks, patterning of search strategies for obtaining information and relevance assessments in choosing retrieved documents. The major claim is that these information activities are systematically connected to task complexity and structure of the problem at hand. The argumentation is based on both theoretical and empirical results from studies on information retrieval and seeking.

Why are online catalogs hard to use? Lessons learned from information-retrieval studies

Research in user behavior on online catalogs is in its early stages, but preliminary findings suggest that users encounter many of the same problems identified in behavioral studies of other types of bibliographic retrieval systems. Much can be learned from comparing the results of user behavior studies on these two types of systems. Research on user problems with both the mechanical aspects and the conceptual aspects of system use is reviewed, with the conclusion that more similarity exists across types of systems in conceptual than in mechanical problems. Also discussed are potential sources of the problems, due either to individual characteristics or to system variables. A series of research questions is proposed and a number of potential interim solutions are suggested for alleviating some of the problems encountered by users of information systems. © 1986 John Wiley & Sons, Inc.

Why are online catalogs hard to use? Lessons learned from information-retrieval studies

Why are online catalogs hard to use? Lessons learned from information-retrieval studies